Demand and Supply Structure for Food in Asia

Sustainability 2011, 3, 363-395; doi:10.3390/su3020363

sustainability ISSN 2071-1050

www.mdpi.com/journal/sustainability

Article

Demand and Supply Structure for Food in Asia

Kanichiro Matsumura

Department of Applied Informatics, School of Policy Studies, Kwansei Gakuin University, Sanda,

Hyogo, 669-1337, Japan; E-Mail: [email protected]; Tel.:+81-79-565-9026;

Fax: +81-79-565-7605

Received: 6 December 2010; in revised form: 17 January 2011 / Accepted: 19 January 2011 /

Published: 31 January 2011

Abstract: In the late 1990s, the author conducted research entitled ―Modeling the demand

and supply structure for food in Asia‖. The research was based on a system dynamics

method and, using time series datasets up to 1998 to estimate the parameters, tried to figure

out the demand and supply structure for food until the year 2010. In this paper, the author

introduces an overall research structure and compares previous study results with the latest

statistical data provided by the Food and Agricultural Organization, United Nations (FAO).

Keywords: food demand and supply structure; economic development

1. Introduction

Agriculture has promoted the increase of food supply during the past 30 years in the world. The

yields have been rising remarkably, especially among developing countries. Almost half of the world

population exists in the Asian countries. Economic development is progressing in Asian countries such

as China and India. In the late 1990s, the author developed ―The demand and supply structure for food

in Asia‖ and forecasted until 2010. The model shows that demand for food would exceed its supply by

the year 2010 in China and India. In this paper, the author introduces an overall research structure and

compares previous study results with latest statistical data provided by the Food and Agricultural

Organization, United Nations (FAO). This paper is based on the author’s series of four researches [1-4]

based on the System Dynamics Method [5]. The author gives lectures to those interested in modeling

methodology and finds this paper will be useful for participants to access in open access format.

OPEN ACCESS

Sustainability 2011, 3

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2. Understanding Food Intake

2.1. Datasets Used

A future population dataset can be obtained from the United Nation’s world population prospects

on their web-site and provides the high, medium and low projections of population [6]. IMF provides

time series datasets such as population and Gross Domestic Product (GDP) [7]. The FAO provides the

database related to food [8]. This paper uses the database published by the former Japan Association

for International collaboration of Agriculture and Forestry [9]. It covers aspects such as land use,

population, labor, agricultural production, production index, calorie based food supply, farm tractor,

fertilizer, import, export, and, trade index. It can handle global scale agricultural databases at one time

and is a very useful tool. However, a revised version has not yet been published and the sequel version

is long awaited.

2.2. Food Demand and Income Changes

The change in consumers’ food consumption is divided into the ―quantitative change‖, in which the

amount of consumption increases, and the ―qualitative change‖ in which the proportion of meat and

eggs in meals increases. Changes in the composition of food intake per capita per day, as seen in the

annual report on the family income and expenditure survey, reveal that, with the change in living

standards, food preferences are shifting from meals based on cereals and starch to meals based on

livestock products. Specifically, it shows that the intake of rice and potatoes decreases, while the

intake of oils and fats, sugar and wheat increases. Comparisons of per capita GDP in current US dollar

terms and of purchasing power parity (PPP) conversion rates and annual food intake of Japan, Korea,

China, Thailand, Malaysia, the Philippines, Indonesia, India, Pakistan and Bangladesh are shown in

Figure 1. The total intake of food levels off at about 500 kg around the time when PPP-based US dollar

values of per capita GDP exceed ten thousand dollars.

Figure 1. Per-capita GDP and food intake, data from [7,9].


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The relation between the per capita national income and the rate of the intake of starchy food on a

country-by-country basis shows that the rate of the intake of starchy food decreases with an increase in

income. It is said that with an increased national income, the eating habits westernize, go upscale and

modernize, and there is a change from the life in which human beings directly consume cereals as

primary products, to a life in which cereals are given to livestock and human beings consume the

protein of the livestock products. However, in Japan, the intake of meat is 33 percent of that consumed

in the U.S., the intake of milk and dairy products is 20 percent of that in Sweden, and the intake of

animal fat is 33 percent of that in France at present, which proves that the above cannot always be

accounted for only by economic factors. Provided that the demand is a function of income and price,

the following equation is obtained. Income (Price) elasticity means that if the income (or price of food)

changes, how much the demand will change.

log D = K + a logI + b logP

D: Demand, K: Constant, a: Income elasticity, I: Income, b: Price elasticity, P: Price

3. Model Concept

3.1. Using System Dynamics

In this research, a model construction is conducted, based on system dynamics by using a

spreadsheet program of Microsoft Corporation, which boasts a high penetration rate as business

software and offers an abundant variety of add-in software for statistical analyses. It is based on three

time points of the time axis, time point J (past), time point K (present) and time point L (future).

Furthermore, the time length from time point J to time point K and the time length from time point K

to time point L are defined as JK and KL respectively. The author takes up the problem on the increase

and decrease in population. Population dynamics are said to follow the pattern: high birth and

mortality rates→a high birth rate and a low mortality rate→ low birth and mortality rates. The

relationship of Pop.K, the population at time point t, to Pop.J, the population at time point J is

represented by PopIN.JK, a population increase between time point J and time point K, and PopOut.JK,

a population decrease between time point J and time point K and is represented as follows:

Pop.K = Pop.J + PopIN.JK − PopOut.JK

In addition, if PopIN.JK and PopOut.JK representing a population increase and a population

decrease respectively are a function of per-capita income (GDP/Pop), the following relationships are

derived and the alphabet ―f‖ represents a function.

PopIN.JK = f(GDP/Pop)

PopOut.JK = f(GDP/Pop)

The description of this model, provided by using the spread sheet program ―Excel‖, is as shown in

Table 1. The population in column A can be represented by a population increase in column B. Row

refers to the time differences.


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Table 1. Demographic dynamics on the spreadsheet program.

Column A Column B Column C Column D

*Population *Population Increase *Population Decrease *Income

Row 1 Pop.J PopIN.JK PopOut.JK GDP.J

Row 2 Pop.K PopIN.KL PopOut.KL GDP.K

Row 3 Pop.L GDP.L

3.2. Basic Structure of Model

The hypotheses can be set up that food demand increases with economic development (increase in

income), and food supply is subject to environmental constraints and the influence of land areas used

for it. Think about a structure in which demand induces supply. The values of per capita real gross

national expenditure on a country-by-country currency basis (compared to the base year, 1990) are

used as an indicator of an income level so that it might be unaffected by fluctuations in exchange rates.

In addition, to explain the value of industrial production, the conception of a production function is

introduced. The value of industrial production is determined by capital stock and labor input. A work

force (population × the percentage of work force) is used as the labor input. If a capital investment is

made, capital stock increases. Part of an added value that is newly created (the value of production) is

added to the capital stock as a new capital investment. Assume that there are functional relations

among the above-mentioned factors as follows and shown in Figure 2.

(capital stock) = f(capital investment)

(value of production) = f(capital stock, work force)

(work force) = f(population, percentage of work force)

Birth and mortality rates that determine demographic dynamics are affected by living standards.

(population) = f(birth rate, mortality rate)

(birth rate) = f(value of production)

(mortality rate) = f(value of production)

Assume that per capita food consumption varies according to the value of per capita real gross

national expenditure, an indicator of the living standard. Namely, the following relation holds.

(food consumption per capita) = f(value of production)

(country based food consumption) = f(food consumption per capita, population)

Food supply is calculated from agricultural land area and land productivity. The land productivity

per unit area is affected by factors such as improvement of seeds, the amount of fertilizer applied and

the weather. An agricultural land area can be affected by changes in climate conditions, as well as the

diversion of the land to an industrial property, housing land, or the like, and cultivation of undeveloped

land. Namely, the following relations hold.

(food supply) = f(agricultural land area, productivity)

(agricultural land area) = f(industrialization, development of agricultural land area, water supply)

(land productivity) = f(amount of fertilizer applied)


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Figure 2. Basic structure of model.


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4. Food Demand Modeling

4.1. Basic Concept

To represent quantitative changes and qualitative changes by a model, some items increase and

others decrease in consumption with increases in income. Assumptions are made about the relation

between consumption and income item by item using country-by-country time-series data. Derive the

equation representing the relations among per-capita GDP, calorie intake from staples and income

estimated on a country-by-country basis. 1990-based real values of country currency based GDP is

used and it might be free from the influence of fluctuations in exchange rates. To estimate food

demand of each country, author refers to the mechanism of producing income. Added values are

generally produced by capital and labor and the concept of a production function is introduced. Capital

stock data is estimated using IMF statistics. Author estimated the equation representing the relation

between capital stock and labor input. Income elasticity of calorie consumption of food is obtained on

a country-by-country basis. Author constructs a country-by-country model of food demand in which a

capital investment and the percentage of work force are taken as an exogenous variable to achieve the

construction of a model that can represent actual values.

4.2. Estimation of Capital Stock Data

Author used the time-series data of Gross Fixed Capital Formation in IMF statistics to prepare

capital stock data and estimate a production function of each country in Asia [7]. Assume that capital

is depreciated on a straight-line basis over ten years to a residual value of 10 percent of the original

value. According to this assumption, if an investment of 100 dollars is made in the year t, it is

depreciated over 10 years to a residual value of 10 dollars and the depreciation costs are 9 dollars per

year. Likewise, the calculations are made on the assumption that it is depreciated over ten years

following these 10 years to a residual value of 10 percent of the original value. Assume that the capital

stock data of a certain year is calculated by totaling the residual values of the year and the former years.

For example, the capital stock data of the year 1997 is calculated by totalizing the residual values of,

respectively, the years 1965, 1966 and 1967. Since the data was a nominal value, it was changed to the

1990-based real value by using a 1990-based deflator. For the influence of fluctuations in exchange

rate to be excluded, GDP on a country-by-country currency basis is used. In calculating capital stock

data, the data before 1965 is not considered, not only because author does not have enough data, but

also because author believes that the error at the beginning decreases with the passage of time in light

of the fact that the increasing amount of capital is accumulated with each passing year in Asian

countries, and therefore concludes that the data before 1965 can be ignored. Author estimated the

capital stock of India, China, Indonesia, South Korea, Malaysia, Myanmar, Japan, the Philippines, Sri

Lanka, Thailand and Singapore. The period, at which the capital stock data used for the estimation

starts, varies according to the country’s state of data arrangement. Author could not obtain Gross

Capital Formation data for Vietnam, Laos and Mongolia from IMF statistics, so the author gave up

estimating the capital stock data of these countries. Table 2 shows a method of estimating capital stock

data in Japan as an example. The author estimated the capital stock data of other countries using


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―Gross Capital Formation‖ such as India, China, Indonesia, Korea, Malaysia, Myanmar, Japan,

Philippines, Sri Lanka, Thailand and Singapore.

Table 2. The estimation of capital stock data: Japan, data from [7]. (Unit: Billion of Yen).

Year Gross Capital

Formation

Stock (Unit:

Current year) Depreciation

1965 9,782 9,782 9,782

1966 11,562 20,464 8,902 11,562

1967 14,288 32,831 8,021 10,521 14,288

1968 17,567 47,191 7,141 9,481 13,002 17,567

1969 21,441 63,844 6,260 8,440 11,716 15,986 21,441

1970 26,043 83,169 5,380 7,400 10,430 14,405 19,511

1971 27,637 101,745 4,500 6,359 9,144 12,824 17,582

1972 31,524 121,720 3,619 5,319 7,858 11,243 15,652

1973 40,938 148,272 2,739 4,278 6,572 9,662 13,722

1974 46,695 176,897 1,859 3,237 5,287 8,081 11,793

1975 48,136 202,760 978 2,197 4,001 6,500 9,863

1976 51,945 228,980 978 1,156 2,715 4,919 7,933

1977 55,982 255,603 978 1,156 1,429 3,338 6,003

1978 62,147 284,638 978 1,156 1,429 1,757 4,074

1979 70,171 317,685 978 1,156 1,429 1,757 2,144

1980 75,821 351,996 978 1,156 1,429 1,757 2,144

1981 78,908 7,464 978 1,156 1,429 1,757 2,144

4.3. Estimation of Production Function

The presence of factories and workers creates added value. This is a basic concept of a production

function. On the assumption that GDP is determined by capital stock and labor input, author did an

estimation by using a Cobb-Douglas production function having these two variables as independent

ones. Private capital stock and public capital stock were collectively defined as capital stock. A capital

investment can be grasped as Gross Capital Formation per year, and GDP varies according to the

amount of investment. 1990-based real GDP data on a country-by-country basis is used. In this paper,

nominal GDP value is used. In general, the values computed by multiplying wages by the number of

workers are used as labor input, but it was difficult to obtain detailed time-series data of wages, so that

author used the number of workers as an indicator of labor input for the estimation. Author estimated

the Cobb-Douglas production function represented by the following equation having gross production

Y as a product (explained variable) and capital stock K and labor input L as a production factor

(independent variable).

Y = A × Kα

× Lβ (1)

Given that labor and capital each get a reward equally according to their own marginal productivity

on the assumption of perfect competition, the distribution ratio between capital and labor remains

constant, which is consistent with the fact that the actual distribution ratio is stable in the long run.

Therefore, the constraint that the Cobb-Douglas production function has the property of constant


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returns to scale, namely, α + β = 1 is placed. Applying logarithmic transformation to Equation (1),

author obtains Equation (2).

LnY−LnL = α(LnK−LnL) + LnA (2)

Author estimated a production function in which (LnY−LnL) is an explained variable, LnA is a

constant term, and (LnK−LnL) is an explanatory variable. Regression analysis is applied and estimated

results are shown in Table 3. The results suggest the economical structure of countries. For example, in

Myanmar and Philippines, the economic structure relies on labor. In China and Korea, the economic

structure relies on stocks.

Table 3. Estimation of production function. ** p < 0.01, * p < 0.05.

LnA Capital stock

coefficient (α)

Labor input

coefficient (β)

Correlation

coefficient

Significance

Level

F-value

India −1.017 0.405 0.595 0.862 ** 162.69

China −2.938 0.566 0.434 0.936 ** 175.42

Indonesia 0.601 0.348 0.652 0.972 ** 833.36

Korea 0.793 0.580 0.420 0.984 ** 1649.14

Malaysia 1.369 0.456 0.544 0.906 ** 210.95

Myanmar 1.792 0.273 0.727 0.972 ** 91.91

Japan 0.619 0.502 0.498 0.960 ** 509.25

Philippines −2.180 0.267 0.733 0.826 ** 128.32

Sri Lanka 3.728 0.349 0.651 0.862 ** 149.75

Thailand −1.477 0.526 0.474 0.972 ** 383.17

Singapore 1.676 0.469 0.531 0.940 ** 405.73

4.4. Income and Population Changes

The world population undergoes the following process with increasing income: high birth and

mortality rates → a high birth rate and a low mortality rate → low birth and mortality rates. The more

income results in a decreasing birth rate. In Asian countries, the birth rate begun to decline prior to

developing countries in other regions, so that the rate of increase in population remains at a level lower

than in those countries. Note, the equation representing the relation between income and demographic

dynamics. Estimate the relation between demographic dynamics and income on a local currency basis

country by country. With regard to the estimation of the single regression analysis, assuming that the

population of each country in Asia as of the year t + 1, is determined by the real GDP in the year t,

author estimates constant and coefficient shown in Table 4. GDP per capita changes result in increase

of population. It is important in curbing population increases in developed countries for developed

countries to provide medical aid and educational aid especially in raising the women’s literacy rate.

4.5. Income and Food Demand

Author estimated the equation representing the relation between food demand and per capita

income on a local currency basis country by country by performing a simple regression analysis of


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time-series data. The results of this analysis are shown in Table 5. GDP per capita changes result in

change of food intake.

Table 4. Relationships between GDP per capita and increase in population. ** p < 0.01, * p < 0.05.

Constant Coefficient Correlation coefficient Significance Level

Japan 94.55 0.000074 0.925 **

Singapore 1.97 0.000012 0.925 **

Thailand 33.41 0.011856 0.878 **

Sri Lanka 7.95 0.000029 0.957 **

Philippines 14.94 0.043393 0.909 **

Pakistan 46.67 0.080327 0.996 **

Nepal 3.60 0.000163 0.694 **

Myanmar 16.88 0.000148 0.854 **

Mongolia 1.08 0.000106 0.495 *

Malaysia 8.63 0.000084 0.974 **

Korea −389,104.04 12,634.882670 0.867 **

Indonesia 100.96 0.000427 0.972 **

India 360.50 0.097947 0.963 **

Fiji 0.50 0.000119 0.493 *

China 914.31 0.139210 0.982 **

Bangladesh 5.05 0.018032 0.994 **

Table 5. Calorie intake and income elasticity. ** p < 0.01, * p < 0.05.

Constant Coefficient Correlation

coefficient

Significance

Level

F−value

Japan

Rice 319.19 −0.00000163 0.1319 0.304

Fats and oils 65.00 0.00008124 0.9763 82.513

Sugar and honey 175.24 0.00004722 0.9606 48.764

Fish 132.80 0.00002266 0.8755 14.059

Meat and internal organs −60.38 0.00007886 0.9952 ** 416.074

Others 1,545.50 0.00001073 0.0363 0.075

Thailand

Rice 1,170.06 0.00116682 0.0229 0.070

Fats and oils 73.59 0.00150058 0.5432 3.567

Sugar and honey 90.93 0.00288040 0.3961 1.968

Meat and internal organs 103.09 0.00047388 0.9190 * 34.016

Nuts (except for oil) 128.74 −0.00031602 0.3617 1.700

Others 530.858 0.00111570 0.0690 0.4444

Sri Lanka

Rice 1,051.00 −0.00000836 0.0671 0.360

Wheat 510.76 −0.00001156 0.2249 1.451

Fats and oils 142.81 −0.00000330 0.1866 1.147

Sugar and honey −466.41 0.00003729 0.6854 * 10.894



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Table 5. Cont.


coefficient

Significance

Level

F−value

Philippines

Rice −306.52 0.07107081 0.5672 3.931

Wheat 220.39 −0.00278479 0.0018 0.005

Sugar and honey −163.79 0.02441402 0.1746 0.635

Maize −1,361.50 0.08940807 0.7836 10.866

Meat and internal organs 0.58 0.00789589 0.1041 0.349

Pakistan

Rice 896.60 −0.09534395 0.6282 5.068

Wheat 1,082.51 −0.00132097 0.0000 0.000

Fats and oils −77.43 0.05336148 0.7777 * 10.494

Sugar and honey −123.75 0.05394770 0.5980 4.463

Milk and products (Except

butter)

72.97 0.01279381 0.8142 13.150

Nepal

Rice 4,108.56 −0.00059460 0.7603 9.516

Wheat 184.37 0.00002124 0.0571 0.182

Fats and oils 44.03 0.00001209 0.3774 1.819

Maize 502.94 −0.00000953 0.0009 0.003

Millet and sorghum −153.25 0.00004354 0.2919 1.237

Others −67.006 0.00008071 0.8873 ** 47.2570

Myanmar

Rice 1601.97 0.00010277 0.2570 1.038

Fats and oils 121.66 0.00002018 0.0464 0.146

Sugar and honey 163.99 −0.00002924 0.0625 0.200

Pulses 420.62 −0.00009884 0.2013 0.756


Others 122.77 0.00004881 0.2504 2.004

Malaysia

Rice 352.88 0.00007875 0.8054 * 12.415

Wheat 334.37 −0.00001422 0.1264 0.434

Fats and oils 217.11 0.00004607 0.6256 5.013

Sugar and honey 272.32 0.00001291 0.8185 13.529

Meat and internal organs 78.43 0.00002158 0.7928 * 11.479

Korea

Rice 1400.34 −0.00006831 0.8871 * 23.574

Wheat 502.55 −0.00003386 0.8729 0.873

Fats and oils 43.04 0.00005557 0.9174 * 33.324

Sugar and honey 95.27 0.00004281 0.8974 * 26.242

Alcohol 346.71 −0.00002030 0.9551 ** 63.786


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Table 5. Cont.


coefficient

Significance

Level

F−value

Indonesia

Rice 1194.18 0.00030683 0.8762 21.228

Fats and oils 210.51 −0.00000161 0.0014 0.004

Nuts 119.34 0.00008222 0.6552 5.701

Root stock 176.81 −0.00001475 0.0252 0.078

Maize 141.83 0.00001270 0.0113 0.034

Others 182.79 0.00020787 0.8949 ** 51.064

India

Rice 433.39 0.04950524 0.6823 * 12.888

Wheat 128.31 0.05270481 0.5488 * 7.298

Fats and oils 39.80 0.02162969 0.7555 ** 18.544

Sugar and honey 121.03 0.01672475 0.8489 ** 33.702

Millet and sorghum 312.68 −0.01809520 0.2884 2.431

Others 239.95 0.05784007 0.7954 ** 23.332

China

Rice 767.58 0.12010483 0.6301 5.110

Wheat 551.07 0.03479922 0.1805 0.661

China (Continued)

Meat and internal organs 3.14 0.14373471 0.9455 ** 51.999

Maize 93.36 0.06971595 0.6589 5.796

Root stock 190.88 −0.02409035 0.4163 2.140

Author used the estimated equation representing the relation between variables to construct a food

demand model for the Asian region on a country-by-country basis by the System Dynamics (SD)

method. GDP is determined by capital stock and labor input. Increases in capital stock result from

additional capital investments. In this regard, depreciation costs are considered. Assume that birth and

mortality rates which determine demographic dynamics are determined by GDP. In this paper, assume

that per capita item-by-item consumption of major food (on a caloric basis) is affected by per capita

GDP (on a country-by-country currency basis and nominal value). The prefix of each variable

indicates a country. In the construction of a model for each Asian country, a coefficient of a variable is

omitted because it only varies from country to country in general. As mentioned before in Section 3.1,

the ―J‖, ―K‖, and ―L‖ suffixes on the variables, indicate, respectively, the past, present and future in

the passage of time. Furthermore, ―JK‖ and ―KL‖ refer to the time passage from the past to present and

the time passage from the present to future respectively. ―DT‖ represents a unit of time, which is one

year here. If ―L‖ is placed to the left side of the equation, the equation is a level equation for

determining the present value by calculation of the difference produced during the time length between

the prior time point and the present. ―R‖ is prefixed to a rate equation, and ―A‖ is prefixed to an

auxiliary equation. An auxiliary equation cannot depend on other auxiliary variables that are not yet

calculated. Population (JaPoP) at the present time point (K) is expressed by a net increase obtained by

subtracting the number of deaths from the number of births for the period, from the past (one year ago)


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to the present. As an initial value of population (expressed in millions), the figure from the year 1965 is

used. The net increase obtained by subtracting the number of deaths from the number of births is

determined by per capita real GDP one year ago. In this regard, 1990-based values of real GDP on

country-by-country currency basis are used.

L JaPOP.K = JaPOP.J + (JaPIN − JaPOUT.).JK × DT

N JaPOP = 98.9

R (JaPIN − JaPOUT).KL = 94.55 + 0.0000736839 × JaSEISAN.K/JaPOP.K

Gross product (JaGDP) is calculated from capital stock (JaSTOCK) and labor input (JaROUDO).

As the gross product at this time, the values (benchmark year = CY 1990) on a country-by-country

basis are used. Increases in total capital stock result from additional capital investments (JaTOSI).The

depreciation costs are considered in the assumption of capital stock estimation. Labor input is

calculated from population and the percentage of work force. Item-by-Item Food Intake per capita per

day on a caloric basis is calculated item by item from per-capita real Gross National Expenditure. In

this connection, the following JaFOOD1 to JaFOOD6 are arranged in correspondence with the

sequence shown in Table 5.

A JaGDP.K = exp(0.6191) × (JaSTOCK)0.5022 × (JaROUDO.K)0.4978

L JaSTOCK.K = JaSTOCK.J + JaTOSI.JK

A JaROUDOU.K = JaPOP.K × JaRATEROU.K

JaIND.K = JaGDP.K/JaPOP.K

A JaFOOD1.K = 319.2 − 0.163 × (JaIND.K)/10000

A JaFOOD2.K = 64.9 + 8.12 × (JaIND.K)/10000

A JaFOOD3.K = 175.24 + 4.77 × (JaIND.K)/10000

A JaFOOD4.K = 132.8 + 2.26 × (JaIND.K)/10000

A JaFOOD5.K = −60.38 + 7.89 × (JaIND.K)/10000

A JaFOOD6.K = 1545.5 + 1.07 × (JaIND.K)/10000

4.6. Comparison between Actual Values and Calculated Values

Actual values and calculated values of GDP on a country-by-country currency basis are shown in

Figure 3. The calculated values almost represent the actual values. The calculations of changes in the

composition of food intake per capita per day worked out by using the calculated GDP (on a calorie

basis) are shown in Figure 4. This figure shows that the actual values almost correspond with the

calculated values.


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Figure 3. The comparison of actual values with calculated values of GDP: Japan, data from [7].

Figure 4. Per-capita, per day consumption of staples (caloric basis): Japan (Actual values:

left, calculated values: right), data from [9].

5. Land Use Modeling

5.1. Basic Concept

It is important to understand how land use represents an assessment of the effect of human activities

at the micro level, are expressed at the macro level. Japan has an area of 37,770,000 ha (13.7%). A

close look at the changes in agricultural land area will reveal that the areas of some categories, notably

paddy fields, have been increasing while the total agricultural land area has been decreasing, since it

reached a peak of 6,081,000 ha in 1960. The proportion of the agricultural land area in the total land

area decreased from 16.1% to 13.7%. This decrease resulted from enlargement (agricultural land

development), and alterations and abolitions (conversion to factory sites, roads, railways, housing sites,

agricultural and forestry roads) of the agricultural land. While agricultural land has been newly

developing, the other sites have been being replaced with factory sites, roads and residential sites.

Industrialization requires factory workers and a market, which has been increasing the number of city


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dwellers. The agricultural land area has been decreasing by the area equal to that of Hiroshima

Prefecture for the last thirty years. As to the supply of rice, which constitutes the largest proportion of

the food supply of Asia, note the rice acreage and the rice yields released by FAO. The rice acreage

hovers at 130,000,000 ha, and in 1993, it decreased over the previous year. Above all, the decreases in

acreage area in China and India have had a great impact on those in Asia. In consideration of the fact

that soil resources for food production is obtained after many years of labor input and capital

investments, you should have a sense of impending crisis over the rapid loss of soil resources. The

progression of values of per capita real GDP in US dollars and the urban population ratios in Japan,

Thailand, the Philippines, Pakistan, Myanmar, Malaysia, Korea, Indonesia, India and China (in 1990)

are shown in Figure 5. This figure shows the state in which the population is concentrating in urban

areas until per capita real GDP reaches 5000 dollars.

Figure 5. Per capita GDP (dollar basis) and urban population ratios, data from [7,9].

It can be considered that changes in income have brought about changes of the urban population

ratio in Asian countries. Income changes result in ratio changes of urban population. Data from 1973

to 1993 is used to estimate the single linear regression equation. Per capita real GDP on a

country-by-country basis (benchmark year = 1990) is used as income so that it might be free from the

influence of changes in exchange rates. Table 6 shows these relationships in Japan.

(urban population ratio in each of the Asian countries) = f(per-capita income)

Table 6. Income per capita and ratio of urban population: Japan. ** p < 0.01, * p < 0.05.


73.33031 0.0000011493 0.8954 **


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5.2. Changes in Urban Population Ratio and Land Use

Of the equations representing changes in land use and urban population ratio, the equations from

which significant figures can be obtained. Author attempts to explain changes in land use by changes

in urban population ratio.

(Arable land, permanent cropland) = f(urban population ratio)

(Irrigated land) = f(urban population ratio)

(Forest, woodland ) = f(urban population ratio)

(Other categories of land) = f(urban population ratio)

Author used data averages from 1974, 1976, 1979 and 1981, and the data of 1987 to 1993 on

changes in land use and the time-series data at the same time on the urban population ratio to calculate

a regression line by the least squares method. This shows changes in used land area (sq km) at the time

when the urban population ratio changes by one percent. Table 7 shows the changes in urban

population ratio and land use in Japan.

Table 7. Changes in urban population ratio and land use: Japan. ** p < 0.01, * p < 0.05.


coefficient

Significance Level

Arable land, permanent cropland 295,528 −3,234 0.947 **

Irrigated land 196,474 −2,177 0.993 **

Forest, woodland 220,356 403 0.077

Other categories of land −335,838 5,008 0.899 **

Table 8 shows the changes in the usage of each category of land when the ratio of urban population

increases. In Indonesia, Thailand and Malaysia, there has been a tendency to increase areas of arable

land and irrigated land, and efforts have been made to increase food supply in their own countries. In

the Philippines, except in ―Other categories‖, an increase of urban population results in increase of

land use. In Bangladesh, agricultural lands are increasing.

Table 8. Changes in urban population ratio and land use.

Japan India Indonesia China Bangladesh

Arable land, permanent cropland − + + − +

Irrigated land − + + + +

Forest, woodland −

Other categories of land + −

Sri Lanka Thailand Vietnam Philippine Pakistan

Arable land, permanent cropland + +

Irrigated land + + + +

Forest, woodland − − + +

Other categories of land − −


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5.3. Land Use Modeling

The author assumed that changes in land use of each country were affected by the changes in urban

population rate. The change of urban population rate was affected by income per capita. In model

construction of Asian countries, a coefficient of a variable is omitted because it only varies from

country to country in general. The ―J‖, ―K‖, and ―L‖ suffixes on the variables indicate, respectively,

the past, present and future in the passage of time. Furthermore, ―JK‖ and ―KL‖ refer to the time

passage from the past to present and the time passage from the present to future respectively. ―DT‖

represents a unit of time, which is one year here. ―L‖ is placed to the left side of a level equation for

determining the present value by calculation of the difference produced during the time length between

the prior time point and the present time point. ―R‖ is prefixed to a rate equation, and ―A‖ is prefixed

to an auxiliary equation. An auxiliary equation cannot depend on other auxiliary variables that are not

yet calculated. In the land use model constructed this time, the level equation and the rate equation are

used for the estimation of the values of production.

As to Japan, suffix ―Ja‖ represents ―Japan‖, population (JaPoP) at the present time point (K) is

expressed by a net increase obtained by subtracting the number of deaths from the number of births for

the period from the past (one year ago) to the present. As an initial value of population (expressed in

millions), the figure from the year 1965 is used. The net increase obtained by subtracting the number

of deaths from the number of births is determined by annual per capita real GDP as of one year ago.

L JaPOP.K = JaPOP.J + (JaPIN − JaPOUT).JK × DT

N JaPOP = 98.9

R (JaPIN − JaPOUT).KL = 94.55 + 0.0000736839 × JaSEISAN.K/JaPOP.K

Real GDP in the year K (JaGDP.K) is calculated from capital stock (JaSTOCK.K) and labor input

(JaROUDO.K)

A JaGDP.K = exp(0.6191) × (JaSTOCK.K)0.5022 × (JaROUDO.K)0.4978

The urban population ratio (JaCITY.K) in the year K is determined by per capita Gross Product

(JaGDP.K/JaPOP.K) in the same year.

A JaCITY.K = 73.33.31 + (JaGDP.K/JaPOP.K) × 0.0000011493

Likewise, the areas of arable land and permanent cropland (JaKouchi.K), irrigated land

(JaKangai.K) and other categories of land (JaEtc.K) as of the year K, are determined by the urban

population ratio in the same year (JaCity.K).

A JaKouchi.K = 295,528 + (JaCity.K) × (−3,234)

A JaKangai.K = 196,474 + (JaCity.K) × (−2,177)

A JaEtc.K = −335,838 + (JaCity.K) × 5008

Total land area does not change. Each land area is a function of urban population ratio. Judging

from correlation coefficients, the area where co-relation coefficient is lowest is obtained by subtracting

another area with higher co-relation coefficient from total area. For Pakistan, the calculation of the

area of arable land and permanent cropland was done by subtracting the area values of irrigated land,

forest and woodland, and the other categories of land from the total land area. For Myanmar, the


379

calculation of the irrigated land area was done by subtracting the area values of arable land and

permanent cropland, forest and woodland, and the other categories of land, from the total land area.

For Thailand, Malaysia, Indonesia and China, the calculation of the area of the other categories of land

was done by subtracting the area values of arable land and permanent cropland, irrigated land, and

forest and woodland from the total land area.

A JaForest.K = 376,520 − (JaKouchi.K + JaKangai.K + JaEtc.K)

The actual values and calculated values of changes in land use in Japan are compared. In the

obtained land use model, capital stock (of private section and public section) and the percentage of

work force are taken as given (as exogenous variables). There are slight variations in the equation

representing the relation between an urban population ratio and changes in land use, but it can be

safely said that the calculated values of changes in land use almost represent the characteristics of the

changes of actual values.

6. Food Supply Modeling

6.1. Basic Concept

Food supply in each country is divided into crop food supply and animal origin food supply. The

assumption is made that the vegetable food supply can be explained by an agricultural land area and

the amount of fertilizer applied. Assume that the amount of fertilizer applied is a function of income.

With regard to animal food, the assumption is made that the divergence of per capita income of each

country from the average per capita income of all the Asian countries under study on a dollar basis

determines the country’s supply. It is confirmed that author can obtain a model capable of representing

actual values by integrating a food demand model and a land use model for each Asian country that

has already been constructed and a food supply model constructed in this section and calculating the

supply and demand in terms of calories.

6.2. The Amount of Fertilizer Applied and GDP

Assume that the consumption of fertilizer, closely related with the improvement in productivity of

vegetable resources, is determined by GDP.

(fertilizer consumption) = f (real national income on a local currency basis)

This is because the consumption of fertilizer may increase but will, in general, not decrease. Some

countries, which have reached a stabilized level of fertilizer usage, are moving towards reducing the

amount of fertilizer in consideration of groundwater pollution and soil contamination. The result of

estimations for Japan is shown in Table 9.

Table 9. Fertilizer and GDP. ** p < 0.01, * p < 0.05.


3,816.23 −0.0007148 0.960 **


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6.3. Estimation Equation of Food Supply Composition (Cereal Sector)

Author took the item-by-item food supply as an explained variable and trys to explain it by

agricultural land area (arable land and irrigated land) and the consumption of fertilizer per unit area of

agricultural land. The results of the estimation in Japan are shown in Table 10. Author conducted the

same calculation for the Asian countries.

(agricultural land area) = (irrigated land area) + (arable land area)

(the consumption of fertilizer per unit area of agricultural land) = (the consumption of

fertilizer)/(agricultural land area)

(the supply of cereals) = f(the consumption of fertilizer per unit area, agricultural land area)

(the supply of rootstock crops) = f(the consumption of fertilizer per unit area, agricultural land area)

(the supply of pulse) = f(the consumption of fertilizer per unit area, agricultural land area)

(the supply of oil crops) = f(the consumption of fertilizer per unit area, agricultural land area)

Table 10. Crop supply, agricultural land and fertilizer (Japan).

Constant Agricultural Area Fertilizer Consumption Correlation Coefficient

Crops −11,125 0.3159 45,730 0.723

t−value 0.776 3.562 0.123

Root −1,461 0.0739 55,561.11 0.474

t−value 0.276 2.256 0.403

Pulse −62.5179 0.0047 −6,207.00 0.689

t−value 0.220 2.654 0.840

Oil Crops −320.6917 0.0025 7,528.85 0.593

t−value 2.482 3.153 2.240

6.4. Relative Income

With regard to allocation of resources including food, it is important to grasp not absolute income

but relative income that represents the degree of superiority a country possesses over the other

countries. If food produced in the world is equitably distributed among the people throughout the

world, they can live without suffering from hunger. But the fact is that there is a problem of

distribution of wealth and a lot of people are suffering from hunger. Since there is a limit to resources,

it is not the concept of absolute income that represents how much income a country has, but the

concept of relative income that represents how much advantage the country has over its rival countries

in income which is important in the assessment of whether the country can buy the resources. This

section particularly focuses on the problem of whether Japan can import food from abroad if ever rival

countries of Japan in food import assume greater prominence and/or when other countries grow

economically stronger. Author divides real GDP on a local currency basis (benchmark year = 1990) in

each of the countries under study by the exchange rate of the local currency to the US dollar and its

population, to calculate the country’s per capita real GDP on a dollar basis.


381

Figure 6. Per capita GDP (dollar basis) in 1990 and average value, data from [7].

In order to calculate the average value of per capita production of all industries, author defined the

value obtained through dividing the total sum of the production values of the countries in the figures

by the total population as the average value of per capita production of all the industries, which is

represented as ―Average‖ in the figure. The calculation results for 1990 are shown in Figure 6. Author

also divides the sum total of dollar-based GDP of all the countries mentioned above by the total

population of these countries, and defines the obtained value as the average value of per capita real GDP

of these countries. The rate of increase from 1980 to 1990 was especially remarkable.

6.5. Estimation Equation of Food Supply Composition (Livestock Product Sector)

Consumers have to pay attention to the conditions of availability of meat and fish. Author tried to

express the supply of meat and fish based on the divergence of per capita real GDP on a dollar basis in

each Asian country from the average of all the Asian countries under study. Namely, the following

equation holds:

(the meat supply in year t) = f(per-capita real GDP of a country in question—the average of the

values of per-capita real GDP of all the countries under study)

Fish catches are considered to depend greatly on fishery rights related with the problem of fishery

resources and infrastructure, which gives the nation power. Since there is a limit to fishery resources, it

is necessary to grasp not the absolute value but the relative value of the real GDP representing the

national power to buy fishery resources. Namely, the following equation holds, and the estimate

equation of the supply of meat and fish is shown in Table 11:

(the fish supply in year t) = f(per-capita real GDP of a country in question—the average of

values of per-capita real GDP of all the countries under study)

Table 11. Meat and Marine product supply: Japan. ** p < 0.01, * p < 0.05.


Meat 1826.077 0.090 0.755 **

Marine Product 9837.301 0.099 0.282


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6.6. Animal Resources (Dairy Products) Supply Modeling

The supply of dairy products is premised on the raising of livestock. Strictly speaking, it is

necessary to count the number of cattle and sheep for meat, and the number of cattle and sheep for

dairy products separate from each other, but because of data constraints, the supply of milk is

explained by the total number of livestock inclusive of swine and the total number of livestock

exclusive of swine. The results of these estimations are shown in Table 12. The comparison between

the estimation results including swine, and the estimation result excluding swine, show that the latter

result is more elucidatory.

(the supply of milk) = f(the number of cattle, sheep, goats and swine kept as livestock)

(the supply of milk) = f(the number of cattle, sheep and goats kept as livestock)

Table 12. Supply of Milk and number of livestock. ** p < 0.01, * p < 0.05.

Including Swine Constant Coefficient Correlation coefficient Significance Level

Japan −1,546 0.5933552 0.871 **

Thailand −294 0.040505 0.737 **

Sri Lanka 435 −0.0884683 0.131

Philippines 23 0.0006399 0.092

Myanmar −636 0.088447 0.974 **

Malaysia 23 0.0046321 0.831 **

Korea −625 0.2938219 0.902 **

Indonesia −302 0.025305 0.989 **

India −132,814 0.5237391 0.936 **

China −8,154 0.0234959 0.896 **

Excluding Swine Constant Coefficient Correlation coefficient Significance Level

Japan −6,349 2.9540924 0.973 **

Thailand −224 0.0605217 0.790 **

Sri Lanka 438 −0.0933671 0.142

Philippines 25 0.0013269 0.120

Myanmar 212 0.1205692 0.965 **

Malaysia 17 0.0187062 0.845 **

Korea −754 0.8452262 0.618 **

Indonesia −406 0.0358172 0.984 **

India −139,912 0.5610069 0.936 **

China 7,213 −0.0045273 0.106

6.7. Calculation of the Number of Livestock

Assuming that livestock is changed into meat, calculations are made by multiplying the numbers of

cattle, sheep, goats and swine (in thousands) by their respective per-head weights of edible parts. One

head of cattle weighs about 450 to 635 kg. The amount of meat produced per head of cattle is 257.7 kg,

and the number of calories is calculated at 543,758 kcal. Assuming that the weight of dressed carcass

per head of swine is 75.0 kg, the number of calories it produces is calculated at 218,273 kcal. The

average weight of an adult goat is 42.7 kg, from which 12.84 kg of meat can be obtained. Therefore,


383

assuming that the weight of meat obtained per goat is 12.84 kg as mentioned above, the number of

calories is calculated at 23, 112 kcal [10]. The turnover rate of meat supply is defined by using the data

on the supply of meat, the data on the number of livestock of cattle, swine, sheep and goats, and the

amount of meat that can be obtained per head of livestock. The following equation holds:

(the turnover rate of meat supply) = (the supply of meat)/(the amount of meat that can be supplied)

Table 13 shows how to calculate turnover rate of meat supply from the amount of meat that can be

supplied and number of livestock in Japan.

Table 13. Turnover rate of meat supply: Japan. ** p < 0.01, * p < 0.05.

Cow Unit:

Thousand

Sheep Unit:

Thousand

Goat Unit:

Thousand

Swine

Unit:

Thousand

The amount of meat

that can be supplied

(1000 ton)

The supply

of meat

(1000 ton)

Turnover

rate of meat

supply

69/71 3,584 203 6,432 1,272,720 1,616 0.13%

74/76 3,672 13 110 7,720 1,363,190 2,227 0.16%

79/81 4,261 13 66 9,851 1,629,058 3,002 0.18%

87 4,694 27 48 11,354 1,821,452 3,586 0.20%

88 4,667 29 41 11,725 1,834,390 3,601 0.20%

89 4,682 30 37 11,866 1,845,802 3,571 0.19%

90 4,760 31 35 11,817 1,863,254 3,503 0.19%

91 4,873 30 37 11,335 1,866,455 3,422 0.18%

92 4,980 29 35 10,966 1,874,139 3,399 0.18%

93 5,024 27 34 10,783 1,875,593 3,387 0.18%

94 4,989 25 31 10,621 1,857,794 3,334 0.18%

The regression equation is derived in which the turnover rate of meat supply is taken as an

explained variable, and per capita real national income on the basis of the local currency in each Asian

country is taken as an explanatory variable. The following relational equation is derived.

(the turnover rate of meat supply) = f(per-capita real national income on the basis of a local

currency in each Asian country)

Through the above mentioned process, the turnover rate of meat supply has been derived from per

capita real national income, and the supply of milk has been explained based on the relation between

the amount of meat that can be supplied and the total number of livestock shown in Tables 14 and 15.

Table 14. Turnover rate of meat supply and income per capita: Japan. ** p < 0.01, * p < 0.05.


0.0011810 0.0000000002 0.490 *

Table 15. The number of livestock and the amount of meat than can be supplied: Japan.

** p < 0.01, * p < 0.05.

Constant Coefficient Correlation coefficient Significance Level **1%, *5%

−1,987 0.00981 0.9682 **


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The estimation is made by using the following equation, and the calculated number of livestock is

introduced into the estimation of the supply of milk.

(the number of livestock) = f (the amount of meat that can be supplied)

The comparison between actual and calculated food supply in Japan is shown in Table 16. Asian

country’s comparison was also conducted in the same way.

Table 16. The actual values and calculated values of the supply model: Japan. ** p < 0.01, * p < 0.05.

Supply (Actual)

Crops Roots Pulse Oil crops Meat Milk

69/71 17,593 6,795 219 77 1,616 4,697

74/76 16,636 5,313 177 55 2,227 5,031

79/81 14,318 5,342 108 68 3,002 6,526

87 14,527 6,023 136 76 3,586 7,335

88 13,867 5,719 136 67 3,601 7,607

89 14,318 5,632 146 68 3,571 8,059

90 14,449 5,558 154 59 3,503 8,189

91 13,070 5,440 136 52 3,422 8,259

92 14,286 5,348 106 50 3,399 8,576

93 10,737 5,013 113 32 3,387 8,626

Supply (Calculated)

Crops Roots Pulse Oil crops Meat Milk

69/71 23,445 7,486 316 92 2,175 4,947

74/76 21,861 7,096 295 77 2,252 4,861

79/81 20,690 6,779 285 60 2,549 6,471

87 19,395 6,396 278 37 3,272 7,739

88 19,080 6,298 277 31 3,408 7,644

89 18,759 6,204 275 25 3,310 7,680

90 18,436 6,108 273 19 3,358 7,907

91 18,126 6,017 272 14 3,530 8,244

92 17,951 5,971 270 12 3,624 8,551

93 17,798 5,938 267 11 3,836 8,672

7. Results and Discussion

7.1. Introduction

Food demand modeling, land use modeling and food supply modeling have been carried out in

Sections 4, 5 and 6. This section refers to the trends of food supply and demand in Asian countries

towards 2010, combining these models, take an exchange rate that constitutes an exogenous variable

as given. The urban population ratio is determined by GDP per capita. The areas of arable land,

permanent cropland, irrigated land and the other categories of land are determined by an urban

population ratio. An agricultural land area is determined by the areas of arable land and irrigated land.

The consumption of fertilizer is calculated from real national income on a local currency basis. The

supply of cereal, that of rootstock crops, that of pulse and that of oil crops are calculated from their

respective agricultural land areas and the consumption of fertilizer per unit area of agricultural land.


385

The supply of meat and the supply of fishery resources are explained by the divergence of the national

income in each country from the average of that of all the Asian countries on a dollar basis. In each

model, a capital investment, the percentage of work force and an exchange rate are taken as exogenous

variables and used to predict data on capital investments (Gross Capital Formation) and the percentage

of work force to calculate the trends of food supply and demand in a standard case.

7.2. Capital Investment

Gross Capital Formations are added as a new capital investment. The country-by-country capital

investment is made based on GDP deflator that converts nominal GDP into real GDP showing

economic stability of each country. The regression equation, in which the year and a GDP deflator are

taken as an explanatory variable and an explained variable respectively, and derived on a country by

country basis in Asia. Namely, the following equation holds and the results are shown in Table 17.

(a GDP deflator) = f(the year of the Christian era)

Table 17. GDP deflator and year. ** p < 0.01, * p < 0.05.


Japan −5,582.534 2.858 0.938 **

India −7,558.367 3.846 0.891 **

Indonesia −10,430.651 5.292 0.936 **

Korea −9,188.728 4.668 0.947 **

Malaysia −6,082.071 3.108 0.945 **

Myanmar −9,319.282 4.729 0.616 **

Philippines −9,021.459 4.581 0.852 **

Sri Lanka −7,243.423 3.679 0.846 **

Thailand −6,537.953 3.334 0.970 **

Singapore −5,219.231 2.673 0.974 **

China −11,943.865 6.055 0.864 **

The assumption is made that a part of real national income acquired in year t is invested as real

Gross Capital Formation in year t + 1. The equation representing the relationship between real national

income in year t and real Gross Capital Formation in year t + 1 is derived and the solution to the

equation is defined as the rate of capital investment. Namely, the following equation holds:

(the rate of capital investment) = (Gross Capital Formation in year t+1)/(real national income in year t)

The equation representing the relationship between the rate of capital investment obtained as above

and the year-on-year growth rate of real GDP is derived. Namely, the following equation holds and the

estimated equation is shown in Table 18:

(the rate of capital investment) = f (a year-on-year growth rate of GDP)

With regard to the growth rate of real GDP used as an explanatory variable, the predicted growth

rates of real GDP released by Japan Center for Economic Research (JCER) are used [11]. The report


386

from JCER used in this research is the printed version published in 1997. The growth rates of GDP are

shown in Table 19.

Table 18. Rate of capital investment and a year-on-year growth rate of GDP. ** p < 0.01, * p < 0.05.


Japan 0.294 0.672 0.483 **

India 0.179 0.323 0.116

Indonesia 0.360 −1.391 0.124

Korea 0.325 −0.182 0.013

Malaysia 0.336 −0.289 0.025

Myanmar 0.125 0.386 0.216 *

Philippines 0.212 0.147 0.014

Sri Lanka 0.211 −0.174 0.016

Thailand 0.236 0.743 0.108

Singapore 0.448 −0.658 0.104

China 1.287 7.728 0.600 **

Table 19. Growth rate projections for the year 1996, data from [11].

1980 1990 1995 2000 2010 2020

Japan 4.4 4.0 1.2 2.6 2.6 2.1

Asia 6.0 6.9 7.7 6.9 6.8 6.2

China 6.0 8.8 12.0 8.7 7.8 7.0

NEEDS4 8.9 8.3 6.6 6.1 5.2 4.3

ASEAN4 6.9 5.5 6.7 7.2 6.8 6.4

South Asia 3.1 5.7 3.8 5.2 7.5 6.5

Author thinks that there is a trend toward a recession in Asian countries, the lowest of the predicted

GDP growth rates from 1995 to 2020 is used. Assume that the rates of 1.2% (Japan), 3.8% (India),

7.0% (China), 6.4% (Indonesia), 4.3% (Korea), 4.3% (Malaysia), 3.8% (Myanmar), 6.4% (the

Philippines), 3.8% (Sri Lanka), 4.3% (Thailand) and 4.3% (Singapore) are maintained until 2010.

Since the rates of capital investment in year t + 1 could be obtained, the real Gross Capital Formation

can be calculated from the following equation:

(real gross capital formation in year t + 1) = (real national income in year t) × (the capital

investment rate)

Therefore, nominal Gross Capital Formation in year t + 1 can be calculated as below by using a

GDP deflator.

(nominal gross capital formation in year t + 1) = (real Gross Capital Formation in year t + 1)/

(a GDP deflator)

The obtained data on Gross Capital Formation on a nominal data basis is introduced into the

estimation of capital stock data. Country-by-country new capital investments are calculated by repeating

the above-mentioned process per year. As to new capital investments, depreciation costs are considered.


387

7.3. Progression of Percentage of Work Force

The assumption is made that the percentage of work force in each Asian country increases based on

the rate of increase from 1980 to 1992 shown in Table 20.

Gross Capital Formation in IMF statistics is added as a new capital investment. Assume that the

rates of 0.6% (Japan), 2.2% (India), 2.0% (China), 2.4% (Indonesia), 2.3% (Korea), 2.8% (Malaysia),

2.2% (Myanmar), 2.2% (the Philippines), 2.2% (Sri Lanka), 2.5% (Thailand) and 0.6% (Singapore) are

maintained until 2010. The values of real GDP on a local currency basis toward 2010 are calculated by

doing the above-mentioned process. The calculation in a standard case is made on the assumption that

the exchange rates of 1994 will be maintained until 2010.

Table 20. Growth rate of population and work force in Asia, data from [11].

Growth rate of Population Growth rate of work force

1960–

1970

1970–

1980

1980–

1992

1960–

1970

1970–

1980

1980–

1992

Average of East Asia 2.6 2.2 1.6 2.5 3.2 2.2

Korea 2.6 1.8 1.1 3.1 2.6 2.3

Hong Kong 2.6 2.5 1.2 3.3 4.3 2.0

Taiwan 3.1 2.0 1.5 2.6 3.6 2.8

Singapore 2.4 2.0 1.8 2.8 4.3 1.4

Indonesia 2.1 2.3 1.8 1.7 2.1 2.4

Malaysia 2.9 2.4 2.5 2.7 3.7 2.8

Thailand 3.0 2.7 1.8 2.1 2.8 2.5

China 2.3 1.8 1.4 1.7 2.4 2.0

Average of South Asia 2.4 2.2 1.8 2.1

Average of Middle America 2.4 2.0 3.1 2.5

Average of Africa 2.8 3.0 2.4 2.5

Average of Developed Countries 1.1 0.8 0.7 1.2 1.3 0.6

7.4. Calculation of Food Demand in Terms of Calories

Data on food demand obtained from the food demand model for each country represents the food

demand per capita per day on a calorie basis. The amount of livestock feed required to produce 1 kg of

animal food is shown in Table 21.

Table 21. The amount of livestock feed required to produce 1 kg of animal food (Unit: Kg).

Pig Beef Poultry Milk Egg

Required Feed Crops 4.3 3.6 2.7 0.3 2.7

It has been confirmed that this requires four to seven times the quantity of cereals human beings

directly eat. The following calculation is made. When meat is taken as the food demand, the number of

calories is quadrupled. Author takes a year as 365.25 days in view of leap years to calculate the

number of calories on an annual basis. Therefore, the calorie demand represents the per capita annual

calorie demand.


388

7.5. Calculation of Food Supply in Terms of Calories

The data on the supply obtained from Asian food supply model is divided into the categories of

cereals, rootstock crops, pulse, oil crops, meat, dairy products, and aquatic products. The calorie value

per 100 g of each kind of cereal is shown in Table 22.

Table 22. Calorie of Crops per 100 g.

Product kcal/100 g Product kcal/100 g

Wheat 335 Sweet Potato 123

Rice 351 Soybean 428

Maize 350 Sorghum 336

Potato 77 Avena sativa 317

Barley 339 Rye 333

Cassava 150 Millet 307

Thus, the calculations are made on the assumption that cereals and rootstock crops contain

351 kcal per 100 g, pulse contains 400 kcal per 100 g, and oil crops contain 335 kcal per 100 g

according to Table 22. Similarly, the assumption is made that 100 g of meat represents a heat value of

about 200 kcal. Strictly speaking, the assumption has to be made by kind of livestock, but we would

like to address this in the future. Considering that 200 mL of ―3.6 milk‖ from Snow Brand Milk

Product Co., Ltd. contains about 134 kcal, we define the number of calories per 100 g of dairy

products as about 70 kcal. Since the supply is represented in total, it is divided by population each year.

Therefore, this represents the per capita annual calorie supply.

7.6. Predictions of Demand and Supply

Author conducted future projections based on the conditions mentioned above. The projections

were conducted based on the datasets obtained for 1998. Assuming that the percentage of work force

and the exchange rates are taken as given, the constructed model calculates the trends of food supply

and demand of Asian countries on a calorie basis. The food supply and demand per capita per year by

country is calculated and compared with actual data until 1994. It can be seen from these results that

actual values and calculated values are almost consistent. The modeling is conducted based on datasets

provided from FAO Liaison Office in Japan. However, a newer version of this dataset has not been

published. Author obtained crop production data from the statistical database provided by Food and

agriculture Organization (FAOSTAT) and shows per capita’s crop production in Japan, Thailand, Sri

Lanka, Philippines, Myanmar, Malaysia, Korea, Indonesia, India, and China. The FAOSTAT database

has been available since 1961. The population change from 1961 to 2009 is shown in Figure 7. The

latest results of crop supply per capita per year are also shown.


389

Figure 7. Population change from 1961 to 2009, data from [6].

Figure 8 shows the Demand and Supply structure for food in Japan by calorie intake per capita per

year and food supply of cereals and coarse grain. Food intake and food supply will decrease slightly.

Actual dataset of FAOSTAT suggests the increase of crop supply, because of decrease of population.

Figure 8. Demand and Supply in Japan (A) and crop supply per capita (B), data from [8].

A B

Figure 9 shows the Demand and Supply structure for food in Thailand by calorie intake per capita

per year and food supply. Food intake will increase gradually and food supply will decrease

dramatically. However, actual dataset of FAOSTAT suggests the increase of crop supply.


390

Figure 9. Demand and Supply in Thailand (A) and crop supply per capita (B), data from [8].

A B

Figure 10 shows the Demand and Supply structure for food in Sri Lanka by calorie intake per capita

per year and food. Food intake will increase gradually and food supply will decrease gradually.

However, Actual dataset of FAOSTAT suggests the fluctuation of food supply.

Figure 10. Demand and Supply in Sri Lanka (A) and crop supply per capita (B), data from [8].

A B

Figure 11 shows the Demand and Supply structure for food in Philippines by calorie intake per

capita per year and food supply. Food intake will increase dramatically and food supply will also

decrease dramatically. However, Actual dataset of FAOSTAT suggests the steady food supply but

sometimes, sudden decrease, because of typhoons.

Figure 12 shows the Demand and Supply structure for food in Myanmar by calorie intake per capita

per year and food supply. Food intake of actual data and calculated data matches, but as for food

supply, it does not match, because of lack of accuracy. However, Actual dataset of FAOSTAT

suggests the increase of food supply.


391

Figure 11. Demand and Supply in Philippines (A) and crop supply per capita (B), data from [8].

A B

Figure 12. Demand and Supply in Myanmar (A) and crop supply per capita (B), data from [8].

A B

Figure 13 shows the Demand and Supply structure for food in Malaysia by calorie intake per capita

per year and food supply. Food intake and supply will increase. However, Actual dataset of FAOSTAT

suggests the increase of oilcakes dramatically.

Figure 13. Demand and Supply in Malaysia (A) and crop supply per capita (B), data from [8].

A B


392

Figure 14 shows the Demand and Supply structure for food in Korea by calorie intake per capita per

year and food supply. Food intake will decrease slightly and supply will decrease dramatically.

However, Actual dataset of FAOSTAT suggests the dramatic decrease of supply.

Figure 14. Demand and Supply in Korea (A) and crop supply per capita (B), data from [8].

A B

Figure 15 shows the Demand and Supply structure for food in Indonesia by calorie intake per capita

per year and food supply. Food intake and food supply will increase. However, Actual dataset of

FAOSTAT suggests the increase of supply.

Figure 15. Demand and Supply in Indonesia (A) and crop supply per capita (B), data from [8].

A B

Figure 16 shows the Demand and Supply structure for food in India by calorie intake per capita per

year and food supply. Food intake will increase and food supply will decrease. However, Actual

dataset of FAOSTAT suggests that food supply remains at the same level.


393

Figure 16. Demand and Supply in India (A) and crop supply per capita (B), data from [8].

A B

Figure 17 shows the Demand and Supply structure for food in China by calorie intake per capita per

year and food supply. Food intake will increase and food supply will decrease. However, Actual

dataset of FAOSTAT suggests that food supply remains at the same level or increase slightly.

Figure 17. Demand and Supply in China (A) and crop supply per capita (B), data from [8].

A B

7.7. Discussion

Author introduces the modeling demand and supply structure for food based on system dynamics

method. The methodology can be an educational tool for students to understand future modeling.

According to the results mentioned in Section 7.6, in China and India, demands were expected to

exceed supply around year 2010. However, the crop production per capita is steady in India and there

has been a slight increase in China. Fortunately, the author’s forecast seems not to be coming true.

On 5 August 2010, Russia announced that crop production had gone down by 26% from the

previous year and stopped exporting crops [12]. Chinese imports of maize exceeded exports from

January to July, 2010 [13]. It is important to estimate the impact of changes in climatic conditions on

crop yield. Rice production has consistently outpaced population growth. Subsequently, rice plays a

crucial role in supporting continued global population growth. World rice production in 2007 was

approximately 645 million tons, with Asian farmers producing 90% and China and India alone


394

producing 50% of the global rice supply [14]. Author has worked on the relationships between rice

yield and precipitation [15-17] and also developed mapping of rice supply and a demand structure [18].

References and Notes

1. Matsumura, K.; Nakamura, Y. Modeling the demand structure for food in Japan. J. Soc. Environ.

Sci. 1997, 10, 21-28. (in Japanese)

2. Matsumura, K.; Nakamura, Y. Modeling the demand structure for food in Asia. J. Soc. Environ.

Sci. 1998, 11, 49-63. (in Japanese)

3. Matsumura, K.; Nakamura, Y. Modeling the land use in Asia. J. Soc. Environ. Sci. 1999, 12,

27-36. (in Japanese)

4. Matsumura, K.; Nakamura, Y. Modeling the demand and supply structure for food in Asia. J. Soc.

Environ. Sci. 2000, 13, 339-349. (in Japanese)

5. Kobayashi, H. System Dynamics; Hakuto Shobo: Tokyo, Japan, 1988.

6. UN World Population Prospects 2008; United Nations Population Division: New York, NY, USA,

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8. FAOSTAT, 2009; Available online: http://faostat.fao.org/site/567/default.aspx (accessed on 12

December 2010).

9. Outlook of World Food and Agriculture 1995; Former Japan Association for International

Collaboration of Agriculture and Forestry: Tokyo, Japan, 1996 (in Japanese).

10. Sakai, S.; Shimada, Y.; Goto, S. Impacts of Human Activities on Global Environment;

M.Sc. Thesis; Kanazawa Institute of Technology: Ohgigaoka Nonoichi Ishikawa, Japan, 1996

(in Japanese).

11. Asia Economics 1996; The Economic Planning Agency: Tokyo, Japan, 1996; p. 339; Available

online: http://www.jcer.or.jp/ (accessed on12 December 2010).

12. Brown, L.R. Earth Policy Release—Rising Temperatures Raise Food Prices: Heat, Drought, and

a Failed Harvest in Russia; Earth Policy Institute: Washington, DC, USA, 2010; Available

online: http://www.earthpolicy.org/index.php?/plan_b_updates/2010/update89 (accessed on 11

August 2010).

13. Chinese Imports of Maize Exceeded Export; Nikkei shinbun: Tokyo, Japan, 20 August 2010.

14. Kawashima, H. World Food Production and Biomass Energy—The Outlook for 2050; University

of Tokyo Press: Tokyo, Japan, 2008.

15. GPCC Full Data Reanalysis Version 5; Federal Ministry of Transport, Building and Urban

Affairs: Berlin, Germany; Available online: ftp://ftp-anon.dwd.de/pub/data/gpcc/html/

fulldata_download.html (accessed on 21 January 2011).

16. Matsumura, K.; Sugimoto, K.; Lee, Y.W.; Wu, W.; Chemin, R.J.; Shibasaki, R. Precipitation and

its impacts for global scale rice production. In Proceedings of SSMS2010, Kochi, Japan,

4–6 March 2010; Available online: http://management.kochi-tech.ac.jp/?content=journalpaper

(accessed on 21 January 2010).

17. Matsumura, K. Precipitation and its impacts on global soybean yield and CAIFA concept.

Kwansei Gakuin Univ.Soc. Sci. Rev. 2010, 15, (in press).


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18. Matsumura, K.; Hijmans, R.J.; Chemin, Y.; Elvidge, C.D.; Sugimoto, K.; Wu, W.B.; Lee, Y.W.;

Shibasaki, R. Mapping the global supply and demand structure of rice. Sustain. Sci. 2009, 4,

301-313.

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Demand and Supply Structure for Food in Asia